A tailored course, built for your situation
Mastering OWASP for AI/ML Systems in High-Pressure Engineering Environments
A systematic approach to embedding security into AI/ML pipelines without sacrificing velocity
$199 one-time
24-hour access provisioning
30-day money-back guarantee
Hand-built implementation playbook
12 modules. 12 chapters per module. 144 chapters total.
12 modules, each with 12 chapters (144 chapters total), text-based, plus downloadable templates and a hand-built implementation playbook delivered alongside course access.
Last-minute security rework in AI/ML deployments under accelerated release cycles
The situation this course is for
High-performing engineering teams ship fast, but when security validation arrives late, it triggers costly model rework, timeline slips, and friction between ML and AppSec teams. At scale, patching security in after training creates technical debt and weakens stakeholder trust.
Who this is for
Principal-level engineers leading AI/ML systems in large tech firms under public scrutiny, where security missteps carry executive attention and regulatory risk
Who this is not for
Junior developers, standalone cybersecurity analysts, or non-technical compliance staff who don't own the AI development lifecycle
What you walk away with
- Produce security-embedded AI/ML deliverables that pass internal review on first submission
- Automate OWASP ASVS controls within the model deployment pipeline
- Reduce pre-release validation cycles from weeks to under one workday
- Establish consistent security documentation that survives team rotation
- Position completed projects as reference examples for executive risk reporting
The 12 modules (with all 144 chapters)
Module 1. The AI/ML Security Gap in Production Systems
Identify where traditional security frameworks fall short in machine learning environments, especially around data drift, model inversion, and adversarial inputs.
12 chapters in this module
- Defining the difference between application security and ML system security
- Common attack vectors in deployed AI models that bypass standard controls
- How OWASP ASVS was extended for AI/ML use cases right now
- Mapping security risk to model lifecycle phases from training to inference
- Why security rework costs 10x more after model deployment
- Case study: Security incident in a large language model rollout
- The role of the Principal Engineer in closing the security feedback loop
- Understanding the expectations of executive sponsors on AI safety
- Balancing velocity and compliance in high-output engineering cultures
- How efficiency pressure increases technical debt in AI systems
- Identifying blind spots in current security tooling for ML pipelines
- Preparing to integrate OWASP standards into existing workflows
Module 2. Integrating OWASP ASVS into AI Development
Learn how to apply OWASP Application Security Verification Standard controls to AI/ML-specific components like feature stores, model registries, and inference APIs.
12 chapters in this module
- OWASP ASVS Level 1 vs Level 2 requirements for AI systems
- Mapping OWASP controls to data preprocessing stages
- Securing model training environments against poisoning attacks
- Validating model inputs against OWASP Input Validation Rule 4.5
- Applying authentication controls to model serving endpoints
- Enforcing rate limiting on inference APIs using OWASP recommendations
- Logging model decisions for auditability and traceability
- Protecting model artifacts in storage with OWASP access rules
- Encrypting model weights and parameters in transit and at rest
- Implementing secure model rollback and versioning protocols
- Integrating OWASP checks into CI/CD pipelines for ML
- Automating compliance evidence generation for security reviews
Module 3. Threat Modeling for Machine Learning Pipelines
Adapt STRIDE and DREAD methodologies to AI/ML systems, identifying threats unique to training data, model weights, and inference behavior.
12 chapters in this module
- Applying STRIDE to AI model training workflows
- Identifying spoofing risks in federated learning systems
- Detecting tampering in model update mechanisms
- Preventing repudiation in automated decision logs
- Assessing denial-of-service risks in inference APIs
- Evaluating data leakage through model outputs
- Using DREAD to prioritize AI-specific threats
- Threat modeling for multi-tenant model serving platforms
- Identifying insider risks in model development teams
- Mapping adversarial attacks to MITRE ATLAS framework
- Integrating threat modeling into sprint planning
- Documenting threat decisions for executive review
Module 4. Data Integrity and Model Poisoning Defenses
Secure the data pipeline from contamination, ensuring model integrity from ingestion through inference.
12 chapters in this module
- Understanding data poisoning as a security threat vector
- Validating data sources for authenticity and provenance
- Implementing checksums and cryptographic signatures on datasets
- Detecting anomalies in training data distributions
- Securing data pipelines against unauthorized modification
- Applying OWASP recommendations to data lineage tracking
- Protecting against training set bias manipulation
- Using differential privacy to limit data exposure
- Validating third-party data providers for compliance
- Enforcing data access controls in shared environments
- Monitoring for drift in production data inputs
- Creating automated alerts for suspicious data patterns
Module 5. Model Inversion and Privacy Leakage Mitigation
Prevent attackers from extracting sensitive training data through model queries and responses.
12 chapters in this module
- Understanding model inversion attacks on neural networks
- Measuring privacy risk in model confidence scores
- Applying k-anonymity techniques to model outputs
- Limiting output granularity to prevent re-identification
- Implementing query rate limiting to deter probing
- Adding noise to model responses without degrading utility
- Using federated learning to reduce data centralization risk
- Validating model outputs against privacy thresholds
- Auditing model behavior for unexpected data leakage
- Documenting privacy controls for compliance reporting
- Training teams on privacy-aware model design
- Benchmarking privacy protection across model versions
Module 6. Adversarial Robustness in Production Models
Defend against deliberate input manipulation designed to deceive model predictions.
12 chapters in this module
- Understanding adversarial examples in image classification
- Detecting perturbations in text and audio inputs
- Implementing input sanitization at model endpoints
- Using adversarial training to harden models
- Applying defensive distillation techniques
- Monitoring for anomalous input patterns in real time
- Creating fallback mechanisms for suspicious queries
- Testing model robustness with automated adversarial toolkits
- Establishing thresholds for model confidence alerts
- Documenting adversarial defenses for audit purposes
- Integrating robustness checks into model validation
- Updating defenses in response to new attack patterns
Module 7. Automated Security Validation in CI/CD
Embed security checks into ML pipelines to catch issues early and reduce rework.
12 chapters in this module
- Integrating OWASP ZAP into ML testing environments
- Automating static analysis of model code
- Scanning dependencies for known vulnerabilities
- Validating model configuration against security baselines
- Running dynamic security tests on inference APIs
- Generating compliance evidence automatically
- Setting up gated checks in pull request workflows
- Alerting on security violations in build pipelines
- Enforcing security policy through code templates
- Measuring security debt accumulation over time
- Reporting security metrics to engineering leadership
- Optimizing pipeline speed without skipping checks
Module 8. Secure Model Deployment and Serving
Ensure models are deployed in a way that maintains confidentiality, integrity, and availability.
12 chapters in this module
- Securing model serving environments against privilege escalation
- Validating model artifacts before deployment
- Using container scanning to detect vulnerabilities
- Enforcing role-based access to model endpoints
- Implementing mutual TLS for model API calls
- Logging all model interactions for auditability
- Monitoring for abnormal usage patterns
- Protecting against model theft and reverse engineering
- Ensuring high availability during security updates
- Managing secrets and credentials in model environments
- Validating geo-compliance in global model serving
- Documenting deployment procedures for review
Module 9. Model Monitoring and Incident Response
Detect and respond to security incidents in deployed AI systems.
12 chapters in this module
- Defining normal behavior for model inference patterns
- Setting up anomaly detection on model inputs and outputs
- Creating incident playbooks for security breaches
- Establishing escalation paths for model misuse
- Conducting post-incident reviews for AI systems
- Logging model decisions for forensic analysis
- Auditing model behavior for policy violations
- Responding to adversarial attacks in real time
- Communicating incidents to stakeholders
- Updating models in response to new threats
- Maintaining audit trails for compliance
- Training teams on security response protocols
Module 10. Security Documentation and Stakeholder Communication
Produce clear, reusable artifacts that demonstrate security compliance to technical and non-technical audiences.
12 chapters in this module
- Writing security narratives for executive summaries
- Creating evidence packages for internal audits
- Documenting control implementation for regulators
- Producing runbooks for security operations teams
- Generating compliance dashboards for leadership
- Translating technical details for cross-functional teams
- Maintaining version-controlled security documentation
- Aligning security artifacts with OWASP ASVS structure
- Using automation to keep documentation current
- Presenting security posture in strategy reviews
- Responding to peer challenges with source-backed reasoning
- Archiving security decisions for future reference
Module 11. Leading Security Culture in AI Engineering Teams
Influence team practices to prioritize security without slowing innovation.
12 chapters in this module
- Modeling secure development behavior as a Principal Engineer
- Mentoring junior engineers on security best practices
- Integrating security into team OKRs and milestones
- Running effective security design reviews
- Creating lightweight security checklists for sprints
- Encouraging proactive threat identification
- Recognizing secure coding contributions
- Balancing security rigor with development velocity
- Facilitating cross-team security alignment
- Advocating for security tooling investments
- Sharing lessons from security incidents
- Measuring security culture maturity over time
Module 12. Scaling Security Across AI/ML Initiatives
Replicate security success across multiple teams and projects.
12 chapters in this module
- Creating reusable security templates for new projects
- Standardizing security validation across models
- Sharing threat models between teams
- Establishing centralized security review processes
- Automating compliance for common model patterns
- Documenting lessons from past security incidents
- Building internal security champions network
- Integrating security metrics into engineering dashboards
- Scaling secure deployment practices globally
- Maintaining consistency under efficiency pressure
- Updating standards in response to new threats
- Tracking security maturity across the AI portfolio
How this maps to your situation
- Accelerated release cycles under efficiency pressure
- Executive oversight of AI system integrity
- Need to reduce last-minute security rework
- Demand for reusable, automated security validation
Before vs. after
Before
Security validation happens late, triggering rework, timeline slips, and friction between ML and AppSec teams.
After
Security is embedded from the start, enabling first-time approval, reduced cycle time, and executive recognition of delivery rigor.
What's included with your purchase
- 12 modules with 12 chapters each (144 chapters)
- Downloadable templates and worked examples for every module
- Hand-built implementation playbook delivered alongside course access
- 30-day money-back guarantee
Delivery and format
- Course and learning environment access provisioned within 24 hours of purchase
- Hand-built implementation playbook delivered alongside course access
Format: Text-based modules and chapters in the Art of Service learning environment, plus downloadable templates and worked examples for every chapter, plus the hand-built implementation playbook delivered alongside course access.
Time investment: Approximately 90 minutes per module, designed to be completed over 12 weeks with weekend focus.
If nothing changes
Continuing with ad-hoc security practices risks high-severity incidents, regulatory scrutiny, and erosion of executive trust in AI initiatives.
How this compares to the alternatives
Unlike generic OWASP courses, this program is tailored to AI/ML systems in high-output engineering environments, with concrete implementation patterns for Principal Engineers leading technical teams under efficiency pressure.
Frequently asked
Is this course focused on web application security or AI/ML systems?
It focuses specifically on adapting OWASP standards to AI/ML systems, including model security, data integrity, and adversarial robustness.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Who is this course not for?
It is not for junior developers or non-technical staff who don't own the AI development lifecycle.
$199 one-time. Approximately 90 minutes per module, designed to be completed over 12 weeks with weekend focus..
Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.
30-day money-back guarantee·
144 chapters·
Hand-built playbook included·
Account access within 24 hours